⚡️ Speed up method InMemoryDataStore.get_dimensions by 67%

[codeflash-ai]

📄 67% (0.67x) speedup for InMemoryDataStore.get_dimensions in xarray/backends/memory.py

⏱️ Runtime : 533 microseconds → 320 microseconds (best of 106 runs)

📝 Explanation and details

The optimization replaces a nested loop with a single dict.update() call, achieving a 66% speedup by leveraging Python's optimized C implementation.

Key Changes:

• Eliminated inner loop: The original code used for d, s in v.dims.items(): dims[d] = s to iterate through each dimension and assign it individually
• Used dict.update(): The optimized version calls dims.update(v.dims) directly, which performs the same operation but in optimized C code

Why This is Faster:

• Reduced Python bytecode operations: The original approach executes multiple Python assignment operations (dims[d] = s) for each dimension, while update() handles all assignments in a single C function call
• Better memory access patterns: dict.update() can optimize memory operations internally rather than going through Python's interpreter for each key-value pair
• Lower function call overhead: Eliminates the overhead of iterating through items() in Python bytecode

Performance Impact by Scale:

• Small datasets (1-2 variables): 5-25% improvement, as seen in basic test cases
• Medium datasets (10-100 variables): 50-90% improvement, demonstrating the optimization's effectiveness
• Large datasets (500+ variables): Up to 919% improvement in extreme cases with many dimensions per variable

The optimization maintains identical behavior - when dimensions overlap across variables, the last variable's dimension size still wins, preserving the original semantics while dramatically improving performance for dimension-heavy workloads.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 89 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

# imports
import pytest
from xarray.backends.common import AbstractWritableDataStore
from xarray.backends.memory import InMemoryDataStore


# Helper class to mimic variable objects with .dims attribute
class DummyVariable:
    def __init__(self, dims):
        # dims: dict mapping dimension name to size
        self.dims = dims


# ------------------ UNIT TESTS ------------------

# 1. Basic Test Cases


def test_empty_variables_returns_empty_dict():
    # No variables: should return empty dict
    ds = InMemoryDataStore()
    codeflash_output = ds.get_dimensions()  # 947ns -> 899ns (5.34% faster)


def test_single_variable_single_dim():
    # One variable, one dimension
    vars = {"var1": DummyVariable({"x": 10})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()  # 1.36μs -> 1.16μs (17.1% faster)


def test_single_variable_multiple_dims():
    # One variable, multiple dimensions
    vars = {"var1": DummyVariable({"x": 10, "y": 20})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.45μs -> 1.22μs (18.9% faster)


def test_multiple_variables_non_overlapping_dims():
    # Multiple variables, each with unique dims
    vars = {
        "var1": DummyVariable({"x": 10}),
        "var2": DummyVariable({"y": 20}),
        "var3": DummyVariable({"z": 30}),
    }
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.73μs -> 1.51μs (14.6% faster)


def test_multiple_variables_overlapping_dims_same_size():
    # Multiple variables, overlapping dims with same size
    vars = {
        "var1": DummyVariable({"x": 10, "y": 20}),
        "var2": DummyVariable({"x": 10, "z": 30}),
    }
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.68μs -> 1.34μs (25.3% faster)


# 2. Edge Test Cases


def test_multiple_variables_overlapping_dims_different_sizes():
    # Multiple variables, overlapping dims with different sizes
    vars = {
        "var1": DummyVariable({"x": 10, "y": 20}),
        "var2": DummyVariable({"x": 99, "z": 30}),
        "var3": DummyVariable({"y": 42, "w": 1}),
    }
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.81μs -> 1.50μs (20.5% faster)


def test_variable_with_no_dims():
    # Variable with empty dims dict
    vars = {"var1": DummyVariable({})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()  # 1.22μs -> 1.14μs (7.56% faster)


def test_variable_with_dim_size_zero():
    # Variable with dimension of size zero
    vars = {"var1": DummyVariable({"x": 0})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()  # 1.35μs -> 1.19μs (13.6% faster)


def test_variable_with_negative_dim_size():
    # Variable with negative dimension size (should be allowed by function)
    vars = {"var1": DummyVariable({"x": -5})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()  # 1.33μs -> 1.21μs (10.2% faster)


def test_variable_with_non_string_dim_names():
    # Variable with non-string dimension names
    vars = {"var1": DummyVariable({1: 10, (2, 3): 20})}
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.50μs -> 1.22μs (23.0% faster)


def test_variables_is_empty_dict():
    # Explicitly pass empty dict for variables
    ds = InMemoryDataStore(variables={})
    codeflash_output = ds.get_dimensions()  # 903ns -> 899ns (0.445% faster)


def test_variables_is_none():
    # Pass None for variables (should behave as empty)
    ds = InMemoryDataStore(variables=None)
    codeflash_output = ds.get_dimensions()  # 899ns -> 888ns (1.24% faster)


def test_variable_with_shared_dim_names_different_types():
    # Variables with same dim name but different types (e.g., str and int)
    vars = {
        "var1": DummyVariable({"x": 1}),
        "var2": DummyVariable({"x": 2}),
    }
    ds = InMemoryDataStore(variables=vars)


def test_attribute_dict_ignored():
    # Attributes should not affect get_dimensions
    vars = {"var1": DummyVariable({"x": 10})}
    attrs = {"foo": "bar"}
    ds = InMemoryDataStore(variables=vars, attributes=attrs)
    codeflash_output = ds.get_dimensions()  # 1.37μs -> 1.19μs (14.6% faster)


# 3. Large Scale Test Cases


def test_many_variables_many_dims():
    # 100 variables, each with 10 unique dims
    num_vars = 100
    num_dims_per_var = 10
    vars = {}
    for i in range(num_vars):
        dims = {f"dim_{i}_{j}": i * 100 + j for j in range(num_dims_per_var)}
        vars[f"var_{i}"] = DummyVariable(dims)
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims_out = codeflash_output  # 51.4μs -> 31.2μs (64.8% faster)


def test_many_variables_overlapping_dims():
    # 50 variables, all share the same 5 dims, each with different sizes
    num_vars = 50
    num_dims = 5
    vars = {}
    for i in range(num_vars):
        dims = {f"dim_{j}": i * 10 + j for j in range(num_dims)}
        vars[f"var_{i}"] = DummyVariable(dims)
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims_out = codeflash_output  # 10.7μs -> 5.69μs (87.6% faster)
    # Each dim should have the size from the last variable
    for j in range(num_dims):
        pass


def test_large_dim_names_and_sizes():
    # Variables with long dim names and large sizes
    vars = {
        "var1": DummyVariable({"x" * 100: 999_999}),
        "var2": DummyVariable({"y" * 200: 888_888}),
    }
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 1.52μs -> 1.36μs (11.2% faster)


def test_large_number_of_variables_and_dims_mixed():
    # 200 variables, each with 5 dims, some overlapping, some unique
    num_vars = 200
    vars = {}
    for i in range(num_vars):
        # First 3 dims overlap, last 2 are unique per variable
        dims = {f"shared_{j}": i + j for j in range(3)}
        dims.update({f"unique_{i}_{k}": i * 100 + k for k in range(2)})
        vars[f"var_{i}"] = DummyVariable(dims)
    ds = InMemoryDataStore(variables=vars)
    codeflash_output = ds.get_dimensions()
    dims_out = codeflash_output  # 47.5μs -> 27.5μs (73.0% faster)
    # shared dims should have the value from the last variable
    for j in range(3):
        pass
    # unique dims should exist for each variable
    for i in range(num_vars):
        for k in range(2):
            pass


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from __future__ import annotations

from collections import OrderedDict

# imports
import pytest
from xarray.backends.memory import InMemoryDataStore


class Variable:
    """
    Minimal stand-in for an xarray Variable, for our tests.
    Expects a 'dims' attribute: dict of dimension name -> size.
    """

    def __init__(self, dims):
        self.dims = dims


from xarray.backends.memory import InMemoryDataStore

# unit tests

# ----------- BASIC TEST CASES ------------


def test_get_dimensions_empty_variables():
    # Test with no variables: should return empty dict
    ds = InMemoryDataStore()
    codeflash_output = ds.get_dimensions()  # 945ns -> 900ns (5.00% faster)


def test_get_dimensions_single_variable_single_dim():
    # One variable, one dimension
    ds = InMemoryDataStore(variables={"var1": Variable({"x": 10})})
    codeflash_output = ds.get_dimensions()  # 1.29μs -> 1.11μs (16.5% faster)


def test_get_dimensions_single_variable_multiple_dims():
    # One variable, multiple dimensions
    ds = InMemoryDataStore(variables={"var1": Variable({"x": 10, "y": 20})})
    codeflash_output = ds.get_dimensions()  # 1.34μs -> 1.18μs (14.0% faster)


def test_get_dimensions_multiple_variables_disjoint_dims():
    # Multiple variables, disjoint dimensions
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": 10}),
            "var2": Variable({"y": 20}),
            "var3": Variable({"z": 30}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.62μs -> 1.41μs (14.6% faster)


def test_get_dimensions_multiple_variables_overlapping_dims_same_size():
    # Multiple variables, overlapping dimensions with same size
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": 10, "y": 20}),
            "var2": Variable({"y": 20, "z": 30}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.64μs -> 1.35μs (20.9% faster)


def test_get_dimensions_multiple_variables_overlapping_dims_different_size():
    # Multiple variables, overlapping dimensions with different sizes
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": 10, "y": 20}),
            "var2": Variable({"y": 25, "z": 30}),
        }
    )
    # Last variable's size should win
    codeflash_output = ds.get_dimensions()  # 1.61μs -> 1.45μs (11.6% faster)


def test_get_dimensions_variable_with_no_dims():
    # Variable with empty dims
    ds = InMemoryDataStore(variables={"var1": Variable({})})
    codeflash_output = ds.get_dimensions()  # 1.16μs -> 1.11μs (4.42% faster)


def test_get_dimensions_mixed_variables_some_with_no_dims():
    # Some variables have dims, some don't
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({}),
            "var2": Variable({"x": 5}),
            "var3": Variable({}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.52μs -> 1.35μs (12.7% faster)


def test_get_dimensions_order_of_variables_matters_for_dim_size():
    # Last variable's size for a dimension should be used
    ds = InMemoryDataStore(
        variables=OrderedDict(
            [
                ("var1", Variable({"x": 1})),
                ("var2", Variable({"x": 2})),
                ("var3", Variable({"x": 3})),
            ]
        )
    )
    codeflash_output = ds.get_dimensions()  # 1.58μs -> 1.39μs (13.1% faster)


# ----------- EDGE TEST CASES ------------


def test_get_dimensions_non_string_dim_names():
    # Dimension names are not strings (should still work)
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({1: 10, 2: 20}),
            "var2": Variable({(3, 4): 30}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.66μs -> 1.33μs (24.6% faster)


def test_get_dimensions_dim_names_with_special_characters():
    # Dimension names with special characters
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x!": 5, "y@": 6}),
            "var2": Variable({"z#": 7}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.66μs -> 1.37μs (20.9% faster)


def test_get_dimensions_dim_size_zero():
    # Dimension size is zero
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": 0}),
            "var2": Variable({"y": 10}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.49μs -> 1.35μs (11.0% faster)


def test_get_dimensions_dim_size_negative():
    # Dimension size is negative (should still be included)
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": -1}),
            "var2": Variable({"y": 5}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.53μs -> 1.27μs (20.3% faster)


def test_get_dimensions_dim_size_none():
    # Dimension size is None (should be included)
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": None}),
            "var2": Variable({"y": 5}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.51μs -> 1.29μs (16.6% faster)


def test_get_dimensions_dim_size_float():
    # Dimension size is float
    ds = InMemoryDataStore(
        variables={
            "var1": Variable({"x": 3.5}),
            "var2": Variable({"y": 2}),
        }
    )
    codeflash_output = ds.get_dimensions()  # 1.45μs -> 1.36μs (6.83% faster)


def test_get_dimensions_variables_is_ordered_dict():
    # variables is an OrderedDict, order should be respected for overwriting
    ds = InMemoryDataStore(
        variables=OrderedDict(
            [
                ("varA", Variable({"d": 1})),
                ("varB", Variable({"d": 2})),
                ("varC", Variable({"d": 3})),
            ]
        )
    )
    codeflash_output = ds.get_dimensions()  # 1.65μs -> 1.44μs (15.2% faster)


def test_get_dimensions_variable_with_large_number_of_dims():
    # Variable with many dimensions
    dims = {f"dim_{i}": i for i in range(50)}
    ds = InMemoryDataStore(variables={"var1": Variable(dims)})
    codeflash_output = ds.get_dimensions()  # 3.70μs -> 1.50μs (147% faster)


def test_get_dimensions_variable_with_duplicate_dim_names():
    # Duplicate dim names in different variables, last wins
    ds = InMemoryDataStore(
        variables=OrderedDict(
            [
                ("var1", Variable({"x": 7})),
                ("var2", Variable({"x": 9})),
                ("var3", Variable({"x": 11})),
            ]
        )
    )
    codeflash_output = ds.get_dimensions()  # 1.60μs -> 1.37μs (16.5% faster)


def test_get_dimensions_variables_is_empty_dict():
    # variables is explicitly an empty dict
    ds = InMemoryDataStore(variables={})
    codeflash_output = ds.get_dimensions()  # 956ns -> 851ns (12.3% faster)


def test_get_dimensions_attributes_are_ignored():
    # attributes should not affect dimensions
    ds = InMemoryDataStore(
        variables={"var1": Variable({"x": 5})}, attributes={"foo": "bar"}
    )
    codeflash_output = ds.get_dimensions()  # 1.32μs -> 1.12μs (17.9% faster)


# ----------- LARGE SCALE TEST CASES ------------


def test_get_dimensions_many_variables_disjoint_dims():
    # Many variables, each with a unique dimension
    N = 500
    variables = {f"var_{i}": Variable({f"dim_{i}": i}) for i in range(N)}
    ds = InMemoryDataStore(variables=variables)
    expected = {f"dim_{i}": i for i in range(N)}
    codeflash_output = ds.get_dimensions()  # 50.9μs -> 33.9μs (50.2% faster)


def test_get_dimensions_many_variables_overlapping_dims():
    # Many variables, all with same dimension name, size increases
    N = 500
    variables = OrderedDict()
    for i in range(N):
        variables[f"var_{i}"] = Variable({"x": i})
    ds = InMemoryDataStore(variables=variables)
    codeflash_output = ds.get_dimensions()  # 50.6μs -> 32.8μs (54.6% faster)


def test_get_dimensions_many_variables_many_dims():
    # Many variables, each with many dimensions
    N = 100
    M = 10
    variables = {}
    expected = {}
    for i in range(N):
        dims = {f"dim_{j}": i * 100 + j for j in range(M)}
        variables[f"var_{i}"] = Variable(dims)
        expected.update(dims)  # last variable's size for each dim
    ds = InMemoryDataStore(variables=variables)
    codeflash_output = ds.get_dimensions()  # 32.6μs -> 13.2μs (147% faster)


def test_get_dimensions_large_dim_names_and_sizes():
    # Large dimension names and sizes
    N = 1000
    variables = {"var1": Variable({f"dim_{i}": i for i in range(N)})}
    ds = InMemoryDataStore(variables=variables)
    expected = {f"dim_{i}": i for i in range(N)}
    codeflash_output = ds.get_dimensions()  # 38.9μs -> 3.81μs (919% faster)


def test_get_dimensions_large_variables_dict():
    # Large variables dict, all dims overlap, last wins
    N = 1000
    variables = OrderedDict()
    for i in range(N):
        variables[f"var_{i}"] = Variable({"x": i})
    ds = InMemoryDataStore(variables=variables)
    codeflash_output = ds.get_dimensions()  # 98.2μs -> 62.0μs (58.5% faster)


def test_get_dimensions_performance_large_scale():
    # Performance: should run quickly with 1000 variables, 1 dim each
    import time

    N = 1000
    variables = {f"var_{i}": Variable({f"dim_{i}": i}) for i in range(N)}
    ds = InMemoryDataStore(variables=variables)
    start = time.time()
    codeflash_output = ds.get_dimensions()
    dims = codeflash_output  # 100μs -> 66.1μs (52.4% faster)
    end = time.time()


# ----------- MUTATION SENSITIVITY TEST ------------


def test_get_dimensions_mutation_sensitivity():
    # If the function returns only the first variable's dims, it should fail
    ds = InMemoryDataStore(
        variables=OrderedDict(
            [
                ("var1", Variable({"x": 1, "y": 2})),
                ("var2", Variable({"y": 3, "z": 4})),
            ]
        )
    )
    # Last variable's size for 'y' should win
    codeflash_output = ds.get_dimensions()  # 1.55μs -> 1.39μs (11.6% faster)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from xarray.backends.memory import InMemoryDataStore
import pytest

def test_InMemoryDataStore_get_dimensions():
    with pytest.raises(AttributeError, match="'SymbolicBoundedInt'\\ object\\ has\\ no\\ attribute\\ 'values'"):
        InMemoryDataStore.get_dimensions(InMemoryDataStore(variables=0, attributes=''))

Timer unit: 1e-09 s

To edit these changes git checkout codeflash/optimize-InMemoryDataStore.get_dimensions-miyn825i and push.